Multi-sensory content authorship aid

ABSTRACT

A method, computer system, and a computer program product for recommending additional content to an author generating authored content is provided. The present invention may include monitoring a travel location associated with the author. The present invention may include calculating a multi-sensory region based on the travel location and a maximum sense distance value. The present invention may include selecting at least one piece of additional content from a corpus of additional content based on the multi-sensory region. The present invention may include generating a model based on the at least one piece of additional content. The present invention may include selecting a relevant piece of additional content from the data model based on determining a topic associated with the relevant piece of additional content matches an authored topic associated with the authored content. The present invention may include presenting the selected relevant piece of additional content to the author.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to electronic content analysis.

Every day billions of pieces of electronic content are authored andshared. A well-crafted and meaningful piece of authored content isdeveloped in stages. These stages may last a fraction of second or takehours to develop. Often, content development may become stalled andcontent authors may resort to including poor quality elements in orderto complete and release their content.

SUMMARY

Embodiments of the present invention disclose a method, computer system,and a computer program product for recommending additional content to anauthor generating authored content. The present invention may includemonitoring a travel location associated with the author. The presentinvention may also include calculating a multi-sensory region based onthe monitored travel location and a maximum sense distance value. Thepresent invention may then include selecting at least one piece ofadditional content from a corpus of additional content based on thecalculated multi-sensory region. The present invention may furtherinclude generating a data model based on the selected at least one pieceof additional content. The present invention may also include selectinga relevant piece of additional content from the generated data modelbased on determining a topic associated with the selected relevant pieceof additional content matches an authored topic associated with theauthored content. The present invention may then include presenting theselected relevant piece of additional content to the author.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flowchart illustrating a process for aidingcontent authorship according to at least one embodiment;

FIG. 3 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 4 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 5 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 4, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The following described exemplary embodiments provide a system, methodand program product for a multi-sensory content authorship aid. As such,the present embodiment has the capacity to improve the technical fieldof electronic content analysis by providing a multi-sensory basedcontent authorship aid. More specifically, a content authorship aidprogram monitors the locations traveled by a user, calculates amulti-sensory region for the user around the monitored locations,selects related content and social data based on the calculated sensoryregion, generates a multi-sensory model, and then recommends additionalcontent to the author based on the model.

As described previously, every day billions of pieces of electroniccontent are authored and shared. A well-crafted and meaningful piece ofauthored content is developed in stages. These stages may last afraction of second or take hours to develop. Often, content developmentmay become stalled and content authors may resort to including poorquality elements in order to complete and release their content.Furthermore, content authors may release content that could be enrichedif additional information was made available to the content author.Identifying, collecting, and presenting additional content to an authorthat is meaningful from virtually numberless possible pieces ofelectronic content is difficult.

Therefore, it would be advantageous to, among other things, provide adynamic way to automatically collect, filter, and present meaningfulcontent for authors to aid content authorship. More specifically,additional content may be collected from electronic content providersthat pertains to a geographic region that a user's five senses (i.e.,sight, sound, smell, touch, and taste) may have experienced whiletravelling and may be presented to the user in an electronic contentauthorship program (e.g., word processor or social media application).

According to at least one embodiment, the content authorship aid programintegrates with text editors, message composing editors, or any otherprogram that allows content creation. Content authoring programs may,for example, integrate the content authorship aid program as a plugin,addon, or extension. The content authorship aid program may be activatedbased on user interaction via a menu or user interface (UI) in thecontent authoring program, detecting the user has paused while composingcontent (e.g., pause in typing or dictating content) beyond a thresholdamount of time, detecting the user has focused on writing or beguncomposing content, or through other express actions or observed behaviorof the user.

In embodiments, the content authorship aid program may initialize adefault user profile that may be further adjusted according to theuser's individual preferences. The user profile may include maximumsense distance values for each of the five senses (e.g., the sightmaximum distance value may be a 20 foot radius) that are initialized toa default setting that the user may later adjust individually. The userprofile may further include other user preferences, such as likes ordislikes (e.g., a user may omit certain senses, such as taste), contenteditors the user wants the content authorship aid program to beintegrated with, specific modes of activation (e.g., have additionalcontent presented only in response to the user clicking a button in theUI or to automatically present content without express action by theuser), threshold time length during a pause before presenting additionalrelevant content, and so forth.

According to some embodiments, after a user profile is set up, the usermay register or designate a mobile device (e.g., smartphone) withlocation tracking capabilities and consent to have the contentauthorship aid program monitor the user while travelling using thedesignated mobile device. In monitoring user travels, the contentauthorship aid program may record the location (e.g., via globalpositioning system (GPS), location precision/potential error radius,coordinates) and time at a location. According to at least oneembodiment, the aggregate travels of multiple related users may be usedas the basis for determining meaningful additional content. Suchrelation may be established based on various people that are knowncoworkers (e.g., work in the same real estate office or work as a teamof engineers), friends, or are otherwise identified as related.

Thereafter, a multi-sensory region is calculated for the user based onone or more of the five senses and maximum distances specified for eachsense in the user profile. As described previously, specific senses maybe omitted by the user based on the user-provided information in theuser profile. For example, a user may choose to omit smell and taste ifthe user finds such sense related content would be unprofessional, andmay indicate these preferences in the user profile. According to atleast one embodiment, a clustering algorithm, such as density-basedspatial clustering of applications with noise (DBSCAN), may be used toselect region segments, ignore outliers, and generate a geographicsensory region. The sensory region may be represented as a geofencedarea where information is sourced until the user is finished authoringcontent.

Related content is then selected based on the calculated sensory region,content topic, and social data. The content authorship aid program maypull historic location data within a specific timeframe (e.g., within athreshold time from when the user was travelling) from social medianetworks, news services, messaging services, or other content sources.The selection of data may, for example, include native language, images,translations, and textual data. In embodiments, data may be sourced byusing Apache Solr™ (Apache Solr and all Apache Solr-based trademarks andlogos are trademarks or registered trademarks of the Apache SoftwareFoundation and/or its affiliates) spatial queries, using Twitter filtersby location, or using Gnip application programming interface (API) basedon regions and coordinates of the sensory region determined by theregion calculation. Once the data is sourced or collected, the data maybe organized into a data structure that, according to some embodiments,includes at least a source (e.g., social media source), location (e.g.,geographic coordinates), content (e.g., the text comment posted on asocial network), and an author (e.g., an identifier corresponding withthe person that wrote the content). According to at least oneembodiment, natural language processing (NLP) may be employed to analyzethe content in the sourced additional content data and identify thecorresponding sense that may also be added to the data structure.

Based on the selected content, a multi-sensory model is built. Themulti-sensory model may model existing language and frequency in orderto generate additional content for the author. Content may be orderedwithin the model based on weighting senses and overlap of the senses.According to some embodiments, a more complicated model may be generatedwhich may model graphical ontology or fragment frequency and selection.

Finally, content is recommended to the author. In embodiments, contentmay be recommended to the author in response to the author clicking on abutton or after determining the user has paused for a threshold amountof time. The content authoring program (e.g., text editor) may bequeried to determine the subject of the content that is being created.The subject may be determined, for example, by analyzing the text theuser has entered or the heading for the section of content the user iscurrently composing. Based on the determined subject, data may beselected from the model. The selected data may then be loaded into atemplate, such as a sentence or quote format. The completed templatewith additional content data may then be presented to the author.According to some embodiments, the process for selecting additionalcontent may be recalculated after detecting that a paragraph or asentence written by the author is complete in the content authoringprogram.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a content authorship aid program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run acontent authorship aid program 110 b that may interact with a database114 and a communication network 116. The networked computer environment100 may include a plurality of computers 102 and servers 112, only oneof which is shown. The communication network 116 may include varioustypes of communication networks, such as a wide area network (WAN),local area network (LAN), a telecommunication network, a wirelessnetwork, a public switched network and/or a satellite network. It shouldbe appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 3,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer,wearable device or any type of computing devices capable of running aprogram, accessing a network, and accessing a database 114. According tovarious implementations of the present embodiment, the contentauthorship aid program 110 a, 110 b may interact with a database 114that may be embedded in various storage devices, such as, but notlimited to a computer/mobile device 102, a networked server 112, or acloud storage service.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the content authorship aid program 110a, 110 b (respectively) to receive relevant additional contentrecommendations based on a multi-sensory region. The content authorshipaid method is explained in more detail below with respect to FIG. 2.

According to at least one embodiment, the content authorship aid program110 a and 110 b integrates with a text editor, message compositioneditor, dictation software, or other software program 108 used forcontent authoring. Content authoring programs may, for example,integrate the content authorship aid program 110 a and 110 b as aplugin, addon, or extension. In embodiments, the content authorship aidprogram 110 a and 110 b may be a standalone application. In the case ofan integrated program, the content authorship aid program 110 a and 110b may be activated based on user interaction via a menu or userinterface (UI) in the content authoring program, detecting the user haspaused while composing content (e.g., pause in typing or dictatingcontent) beyond a threshold amount of time (e.g., 30 seconds), detectingthe user has focused on writing (e.g., through image analysis of a userface and visual focus using a camera connected to an electronic device),begun composing content (e.g., detecting typing in an editor), orthrough other express actions or observed behavior of the user.According to some embodiments, the content authorship aid program 110 aand 110 b may create a button or other UI feature within the contentauthoring program that the user may click using a mouse, tap with afinger using a touchscreen, or otherwise interact with to begin thecontent authorship aid program 110 a and 110 b.

In embodiments, a user profile is initialized. The content authorshipaid program 110 a and 110 b may initialize a default user profile thatmay be adjusted later according to the user's individual preferences.The user profile may include maximum distance values for each of thefive senses (i.e., sound, sight, smell, touch, and taste) that areinitialized to a default setting that the user may adjust. For example,the content authorship aid program 110 a and 110 b may initialize amaximum sight distance value to be a 20 foot radius, a maximum sounddistance value to be a 100 foot radius, a maximum touch distance valueto be a 3 foot radius, a maximum smell distance value to be a 5 footradius, and a maximum taste distance value to be a 1 foot radius.Thereafter, the user may open the user profile to adjust the maximumdistance values.

For example, the user may right-click on a button within a contentauthorship program corresponding with the content authorship aid program110 a and 110 b and a menu may appear that includes a choice for theuser to adjust their user profile. In response to the user selecting theoption to adjust their user profile, a UI box may be presented on screenthat displays, at least, the five senses and the current maximumdistance values for each sense. The distances may then be adjusted bythe user using a text box, a slider, or other UI feature. For example,the user may adjust the default maximum sight distance value from thedefault 20 foot radius to a 25 foot radius. Thereafter, the user mayselect a button to save the new profile preferences.

Additionally, the user profile may include other user preferences, suchas likes or dislikes, identifying the content editors the user wants thecontent authorship aid program 110 a and 110 b to be integrated with,preferred modes of activation (e.g., have additional content presentedonly in response to the user clicking a button in the UI or toautomatically present content without express action by the user),threshold time length during a pause before presenting additionalcontent, and so forth. The likes and dislikes preferences may includespecific senses (e.g., a user may omit or dislike certain senses, suchas taste, that the user finds to be inappropriate for the content theuser creates), certain subjects (e.g., likes cars and dislikes snow), orboth. According to alternative embodiments, user preferences of sensesor subjects that the user likes or dislikes may be derived based onhistorical user data. The user profile may also include designatedmobile devices (e.g., computer 102) with location tracking capabilities(e.g., global positioning system (GPS) receiver or other locationsensors) that will allow content authorship aid program 110 a and 110 bto monitor the user's travel. When the user selects one or more devicesfor monitoring user travel, the user may be presented with a dialog boxor other notification that obtains user consent for such monitoring. Inresponse to appropriate user consent, the content authorship aid program110 a and 110 b may add the designated device to the user profile foruse in monitoring the user.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary content authorship aid process 200 used by the contentauthorship aid program 110 a and 110 b according to at least oneembodiment is depicted.

At 202, the travel locations of a user are monitored. For each user(i.e., author), locations are recorded using GPS, a location precisionor potential error radius is recorded, coordinates for the location, andan amount of time at the location are recorded. Based on monitoring thelocation, and changes in location, of a mobile device that correspondswith the user, the travel route of the user may be monitored andrecorded. Travel may be monitored by a mobile device and transmitted viaa communication network 116 to a computer 102 that is running thecontent authorship aid program 110 a and 110 b.

For example, the location of user Fred may be tracked by querying a GPSreceiver in the smartphone that Fred designated in his user profile. AsFred commences a short trip in the Boston area from his office to atrain station, Fred's location is monitored based on the changinglocation of Fred's smartphone. Using the collected GPS data, the contentauthorship aid program 110 a and 110 b determines that Fred is in theBoston area and Fred traveled from an office building to a train stationand the route that Fred took. The travel location information may thenbe stored in a data repository, such as a database 114. Morespecifically, Fred's travels may be recorded in a semi-structured formataccording to the example shown below.

-   -   {Fred, Real Estate Office, Stationary, 8 hours—Thursday 8 AM}    -   {Fred, To Yawkey Station, Transitory, 30 minutes—Thursday 4 PM,        Dwell Location: Fenway Park, Lansdowne Street}}    -   {Fred, Wait, Yawkey Station, Stationary, 15 minutes—Thursday        4:30 PM}    -   {Fred, Identified Commute Route, Transitory, 30 minutes—Thursday        4:45 PM, Dwell Location: {Boston Landing, Brookline}}    -   {Fred, Store, Groceries Chestnut Hill, Stationary, 15        minutes—Thursday 5:15 PM}

As shown in the above example, data entries of Fred's travels mayinclude a user identifier (e.g., Fred), the location as a fixed location(e.g., train station) or a route (e.g., identified commute route), usermovement (e.g., stationary versus transitory), the length of time theuser was at the location, a time stamp (e.g., day and hour), and a dwelllocation, if applicable. Routes may be identified over time based onhistorical data, identified by the user in the user profile, or by someother method. Location names may be determined by inputting collectedraw coordinates of locations the user visited into mapping softwarewhich may be running on a server 112 and receiving (e.g., via acommunication network 116) location names from the mapping software.

According to alternative embodiments, the content authorship aid program110 a and 110 b may use multiple users for travel and location data touse for sourcing meaningful content. If the user works at a real estateoffice, fellow coworker travel data may be combined for use in latersteps of the content authorship aid process 200. Additional usersdesignated to pool together may, for example, be specified in the userprofile.

Next, at 204, a multi-sensory region is calculated. According toembodiments, the user profile may be accessed to determine the recordedmaximum sense values (e.g., 100 foot radius for sound). The accessedmaximum sense values may then be used in conjunction with the previouslydetermined travel data to calculate and define a geographic senseregion. For example, if the user profile defined the maximum sightdistance to be a 20 foot radius, a region would be calculated thatencompasses a 20 foot radius along the route the user previouslytraveled as the sight sense region. Likewise, additional sense regionsmay be calculated for the remaining senses. In embodiments, themulti-sensory region may be the aggregate or maximum of the sensoryregions calculated. The multi-sensory region may be represented as ageofenced area where information is sourced until the user is finishedauthoring content.

Then, at 206, content related to the multi-sensory region is selected.The content authorship aid program 110 a and 110 b may access socialnetwork feeds or other electronic content sources or feeds (i.e., corpusof additional content) to obtain raw content data. For example, GNIP maybe used to obtain raw Twitter data that is then saved to a datarepository, such as a database 114. Thereafter, the raw content data maybe fed into a data processing system. For content that has no expresslyidentified originating location, the originating location may bedetermined based on information in the raw content (e.g., a placeidentifier corresponding with a Tweet) and the determined originatinglocation may be added to the raw data.

The content may be further augmented by adding sense labels. Inembodiments, textual electronic content that is in natural language formmay be analyzed using natural language processing (NLP) techniques toparse and break down elements of the textual content to identify a senserelated to the content. The content may be labeled based on predefinedword or phrase mappings or dictionaries. For example, if user contentincluded the word “shiny,” the content may be associated with the sensesight based on a mapping of the word shiny to sight and a “sight” tagmay be added to augment the collected raw data. In other instances,content may include images that are analyzed using image analysisalgorithms to determine a corresponding sense such as sight or othersense. For example, if an image is a zoomed view of an object with abumpy surface, the image may be tagged with the sense “touch.”Additionally, mixed content that includes, for example, images and textmay entail analyzing both the image and text to determine theappropriate sense tags.

The raw content data may be saved in a format such as the format shownin the example below.

{ “Source” : “Twitter” “Location” : {42.317913, −71.008011} “Content” :“Finally a new house on the market...took long enough” “Sense” : “Sight”“Author” : “@Engineer” “Time” : “Thursday 9:12 AM” }

As depicted in the above example, each piece of collected content mayinclude the source (e.g., social media source or blog), geographiclocation (e.g., expressed as coordinates of longitude and latitude), thecontent (e.g., Tweet or other social media posting), the tagged one ormore senses (e.g., sight, sound, taste, touch, or smell), an identifiercorresponding with the author, and a time stamp. It will be appreciatedthat the collected content data may be stored, formatted, and augmentedin various other ways based on design and implementation requirements.

Thereafter, the augmented data may be partitioned into a coordinatesystem based on the geographic locations corresponding with the piecesof data. A clustering algorithm, such as density-based spatialclustering of applications with noise (DBSCAN), may be used to selectregion segments, ignore outliers, and generate a geographic sensoryregion. DBSCAN may be used by the content authorship aid program 110 aand 110 b to analyze geographic points of the user's travels and obtaincontent that is clustered around the selected point and within theboundaries of the multi-sensory region. Alternatively, a query may begenerated to query the collected social media data based on location. Byusing the geofenced boundaries of the multi-sensory region, a data queryor DBSCAN may be used to identify and select relevant content that wasgenerated within the multi-sensory region. Content may be selected bycomparing the originating location of the content to the sensory regionassociated with the sense assigned to the content. For example, if theuser profile specifies that the maximum sight distance value is a 20foot radius, content that was identified as relating to the sight sensewith an originating location within a 15 foot radius of a location theuser visited would be selected while sight content that has anoriginating location of 25 feet from a user location would not beselected. Similar filtering would occur for the rest of the availablecontent. According to at least one other embodiment, relevant contentmay also be selected from within the multi-sensory region if the contentwas also generated within a threshold time of when the user was at agiven location.

At 208 a multi-sensory model is generated from the selected content.After the related content is selected, a multi-sensory data model may,according to some embodiments, be generated according to a predefinedformat. The multi-sensory data model may model the existing language andfrequency in order to generate language. In some embodiments, themulti-sensory data model may model select data based on a certain timeof day (e.g., 7 AM). The model may also include the people (i.e.,authors) that generated content and the frequency of instances ofgenerated content per person. The data model may then identify keywords(e.g., nouns or adjective noun combinations) from the selected contentand, based on dictionaries or NLP techniques, a topic may be assigned tothe identified keywords.

For example, the following content may be selected based on themulti-sensory region.

{ “Source” : “Twitter” “Location” : {42.317913, −71.008011} “Content” :“Finally a new house on the market...took long enough” “Sense” : “Sight”“Author” : “@Engineer” } { “Source” : “Twitter” “Location” : {42.317913,−71.008011} “Content” : “Sale at Grocery Store” “Sense” : “Sight”“Author” : “@Jeanette” }

From the selected content, an exemplary multi-sensory data model may begenerated as follows.

Model - Time - 7 AM People {@Engineer − Frequency = 1, @Jeanette −Frequency = 1} Content Model { House: 1 − {@Engineer} − {Topic: RealEstate}, Market: 1 − {@Engineer} − {Topic: Real Estate}, Food: 1 −{@Jeanette} − {Topic: Food}, }

In the above example model, the model is based on a time (i.e., 7 AM).The model includes a list of people that authored the content that wasselected which, in this case, includes content authored by “@Engineer”and “@Jeanette” and one piece of content was selected from each of them(i.e., frequency is 1). Then, the content model is generated based onthe selected content. Out of the selected content, three keywords wereidentified: “house,” “market,” and “food.” Each keyword appeared once,therefore the “1” after the keyword signifies the word frequency of one.The model then identifies the author(s) that used the keyword in theircontent. Finally, the content is analyzed (e.g., using NLP) to determinethe topic that the content relates to, such as real estate or food. Inalternative embodiments, the model may also indicate the senseassociated with the content.

Content within the content model may be weighted or ordered or both. Forexample, content that is identified as corresponding with more than onesense (e.g., a thunderstorm may correspond with sight and sound) may beweighted higher than content that corresponds with a single sense.Further, certain senses may correspond with greater weights based onpredetermined ranking (e.g., sight weighted heavier than sound) or auser preference stored in the user profile may allow the user to specifysenses that are most important to the individual user.

According to other embodiments, the content authorship aid program 110 aand 110 b may generate more complex models such as a graphical ontologyor fragment frequency and selection.

Then, at 210, relevant content from the multi-sensory model isrecommended to the author. Based on the specific implementation and userpreferences, the content authorship aid program 110 a and 110 b maybegin recommending and presenting additional content in response to theauthor clicking on a UI button, selecting the appropriate menu option,after determining the user has paused for a threshold amount of time,continuously while the user is typing, or other triggering eventcriteria. The content authoring program (e.g., text editor) may bequeried (e.g., through an application programming interface (API)) todetermine the subject of the authored content that is being created. Thesubject or authored topic may be determined, for example, by analyzingthe text the user has entered or the heading for the section of contentthe user is currently composing. In embodiments, text analysis may beaccomplished through the use of NLP algorithms to identify a topic fromnatural language text strings in the document body, heading, the authorexpressly designating a subject, or other methods. When analyzingheadings, the content authorship aid program 110 a and 110 b may use thecontent authoring program API or identify predefined tags correspondingwith heading text.

For example, if an author is writing about the topic of baseball,certain words or phrases may be identified in the text the author isgenerating which are correlated with baseball, such as “shortstop,”“base hit,” and “inning.” Using NLP to determine the presence of wordsrelated to baseball, the content authorship aid program 110 a and 110 bwill designate the current topic to be baseball. Thereafter, themulti-sensory model is searched to select content that is tagged withthe topic of baseball.

Content with the matching (or in some embodiments, most similar) topicis selected for recommending to the author. In instances with more thanone piece of relevant content, the highest weighted or ordered contentis selected. In other embodiments, multiple pieces of relevant contentmay be presented to the author.

The selected content data may then be loaded into a template. Templatesmay include various predefined formats that present the content datatogether with additional data such as the identifier corresponding withthe author of the selected content. According to at least oneembodiment, the selected content may be loaded into a sentence templatethat presents the content in an adjective subject verb pattern. In otherembodiments, the content may be loaded into a quote template that may beformatted to read “As <Author> says, <Quote Selected Content>” oranother defined quote format.

The completed template with selected content data may be presented tothe author within the content editor using the content authorship aidprogram 110 a and 110 b. According to some embodiments, the selectedcontent, formatted according to a template, may be presented within thecontent editor in a designated partition of the UI. For example, withina word processor, a pane may be displayed within the right-hand side ofthe UI. Within the pane, the selected content is displayed to theauthor. In other embodiments, a UI box, such as a dialog box, may appearcontaining the selected content in response to the author clicking abutton corresponding with the content authorship aid program 110 a and110 b.

According to alternative embodiments, the amount of additional contentthat is recommended to the author may be limited to facilitate themaximum amount of organic contribution by the author to their content.For example, selected content may not be presented to the author if thepercentage of the content the author is generating from recommendedadditional content exceeds a threshold percentage.

It may be appreciated that FIG. 2 provides only an illustration of oneembodiment and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements.

According to another embodiment, the content authorship aid program 110a and 110 b may capture natural language segments that describe subjectsfrom different views or perspectives. For example, if the author iswriting on the subject of cars, content may be recommended that includespositive adjectives such as “shiny new car” and content that includesnegative adjectives such as “rusty old car.”

In another alternative embodiment, raw collected content may betranslated according to globalized or localized definitions. Viewers andauthors of content may interpret or define different words, phrases, orslang differently based on their locality. Thus, the target audience forthe content the author is creating may be determined (e.g., fromhistorical demographic data of the audience of previous content from theauthor) and then the appropriate translation may be applied beforepresenting to the selected content to the author.

As described in embodiments above, the content authorship aid program110 a and 110 b may improve the functionality of a computer by enablinga computer to efficiently analyze electronic content data and theneffectively identify relevant additional content for an author. Theamount of possible electronic content data to draw from to suggest to anauthor is enormous. The above embodiments provide a dynamic andefficient way to narrow the pool of candidate data to include contentemanating from locations within sensory perception of the author anddetermine additional content to recommend based on the subject of thecontent the user is currently authoring.

FIG. 3 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.3 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 3. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908 and one or more computer-readable ROMs 910 on one or more buses 912,and one or more operating systems 914 and one or more computer-readabletangible storage devices 916. The one or more operating systems 914, thesoftware program 108, and the content authorship aid program 110 a inclient computer 102, and the content authorship aid program 110 b innetwork server 112, may be stored on one or more computer-readabletangible storage devices 916 for execution by one or more processors 906via one or more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 3, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the content authorship aid program 110 a and 110 b canbe stored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918 and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the content authorship aid program 110 a inclient computer 102 and the content authorship aid program 110 b innetwork server computer 112 can be downloaded from an external computer(e.g., server) via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 and the content authorship aidprogram 110 a in client computer 102 and the content authorship aidprogram 110 b in network server computer 112 are loaded into therespective hard drive 916. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926 andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 4 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and content authorship aid 1156. Acontent authorship aid program 110 a, 110 b provides a way to identifyand present content originating from within a multi-sensory region toaid content authors.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises,”“comprising,” “includes,” “including,” “has,” “have,” “having,” “with,”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but does not preclude the presence or addition of one ormore other features, integers, steps, operations, elements, components,and/or groups thereof.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for recommending additional content toan author generating authored content, the method comprising: monitoringa travel location associated with the author; calculating amulti-sensory region based on the monitored travel location and one ormore of the five senses including sight, sound, smell, touch and tasteand a maximum sense distance value for each sense; selecting at leastone piece of additional content from a corpus of additional contentbased on the calculated multi-sensory region, in which the additionalcontent is in the form of raw data; augmenting and storing the raw datavia tagging a plurality of sense labels, in which the plurality of senselabels are based on the five senses to generate a plurality of augmenteddata; generating a multi-sensory data model based on the plurality ofaugmented data; selecting one or more of the plurality of augmented datafrom the generated multi-sensory data model based on determining a topicassociated with the selected one or more of the plurality of augmenteddata matches an authored topic associated with the authored content; andpresenting the selected one or more of the plurality of augmented datato the author.
 2. The method of claim 1, wherein selecting the at leastone piece of additional content from the corpus of additional contentbased on the calculated multi-sensory region comprises determining anoriginating location associated with the at least one piece ofadditional content is within the calculated multi-sensory region.
 3. Themethod of claim 1, further comprising: initializing a user profile forthe author, wherein the initialized user profile includes a plurality ofmaximum sense distance values, and wherein the plurality of maximumsense distance values includes the maximum sense distance value.
 4. Themethod of claim 3, further comprising: collecting at least one userprofile adjustment from the author; and adjusting the initialized userprofile based on the collected at least one user profile adjustment. 5.The method of claim 2, wherein selecting the at least one piece ofadditional content from the corpus of additional content based on thecalculated multi-sensory region further comprises: determining a senseassociated with the selected at least one piece of additional content;and determining a distance from the originating location to themonitored travel location is less than the maximum sense distance valuecorresponding with the determined sense.
 6. The method of claim 5,wherein determining the sense associated with the selected at least onepiece of additional content comprises comparing a textual content of theselected at least one piece of additional content to a word mapping. 7.The method of claim 1, wherein presenting the selected one or more ofthe plurality of augmented data to the author comprises formatting theselected one or more of the plurality of augmented data according to atemplate and presenting the formatted one or more of the plurality ofaugmented data to the user within a content authoring program the authoris using.
 8. A computer system for recommending additional content to anauthor generating authored content, comprising: one or more processors,one or more computer-readable memories, one or more computer-readabletangible storage media, and program instructions stored on at least oneof the one or more computer-readable tangible storage media forexecution by at least one of the one or more processors via at least oneof the one or more computer-readable memories, wherein the computersystem is capable of performing a method comprising: monitoring a travellocation associated with the author; calculating a multi-sensory regionbased on the monitored travel location and one or more of the fivesenses including sight, sound, smell, touch and taste and a maximumsense distance value for each sense; selecting at least one piece ofadditional content from a corpus of additional content based on thecalculated multi-sensory region, in which the additional content is inthe form of raw data; augmenting and storing the raw data via tagging aplurality of sense labels, in which the plurality of sense labels arebased on the five senses to generate a plurality of augmented data;generating a multi-sensory data model based on the plurality ofaugmented data; selecting one or more of the plurality of augmented datafrom the generated multi-sensory data model based on determining a topicassociated with the selected one or more of the plurality of augmenteddata matches an authored topic associated with the authored content; andpresenting the selected one or more of the plurality of augmented datato the author.
 9. The computer system of claim 8, wherein selecting theat least one piece of additional content from the corpus of additionalcontent based on the calculated multi-sensory region comprisesdetermining an originating location associated with the at least onepiece of additional content is within the calculated multi-sensoryregion.
 10. The computer system of claim 8, further comprising:initializing a user profile for the author, wherein the initialized userprofile includes a plurality of maximum sense distance values, andwherein the plurality of maximum sense distance values includes themaximum sense distance value.
 11. The computer system of claim 10,further comprising: collecting at least one user profile adjustment fromthe author; and adjusting the initialized user profile based on thecollected at least one user profile adjustment.
 12. The computer systemof claim 9, wherein selecting the at least one piece of additionalcontent from the corpus of additional content based on the calculatedmulti-sensory region further comprises: determining a sense associatedwith the selected at least one piece of additional content; anddetermining a distance from the originating location to the monitoredtravel location is less than the maximum sense distance valuecorresponding with the determined sense.
 13. The computer system ofclaim 12, wherein determining the sense associated with the selected atleast one piece of additional content comprises comparing a textualcontent of the selected at least one piece of additional content to aword mapping.
 14. The computer system of claim 8, wherein presenting theselected one or more of the plurality of augmented data to the authorcomprises formatting the selected one or more of the plurality ofaugmented data according to a template and presenting the formatted oneor more of the plurality of augmented data to the user within a contentauthoring program the author is using.
 15. A computer program productfor recommending additional content to an author generating authoredcontent, comprising a computer-readable storage medium having programinstructions embodied therewith, the program instructions executable bya computer to cause the computer to perform a method comprising:monitoring a travel location associated with the author; calculating amulti-sensory region based on the monitored travel location and one ormore of the five senses including sight, sound, smell, touch and tasteand a maximum sense distance value for each sense; selecting at leastone piece of additional content from a corpus of additional contentbased on the calculated multi-sensory region, in which the additionalcontent is in the form of raw data; augmenting and storing the raw datavia tagging a plurality of sense labels, in which the plurality of senselabels are based on the five senses to generate a plurality of augmenteddata; generating a multi-sensory data model based on the plurality ofaugmented data; selecting one or more of the plurality of augmented datafrom the generated multi-sensory data model based on determining a topicassociated with the selected one or more of the plurality of augmenteddata matches an authored topic associated with the authored content; andpresenting the selected one or more of the plurality of augmented datato the author.
 16. The computer program product of claim 15, whereinselecting the at least one piece of additional content from the corpusof additional content based on the calculated multi-sensory regioncomprises determining an originating location associated with the atleast one piece of additional content is within the calculatedmulti-sensory region.
 17. The computer program product of claim 15,further comprising: initializing a user profile for the author, whereinthe initialized user profile includes a plurality of maximum sensedistance values, and wherein the plurality of maximum sense distancevalues includes the maximum sense distance value.
 18. The computerprogram product of claim 17, further comprising: collecting at least oneuser profile adjustment from the author; and adjusting the initializeduser profile based on the collected at least one user profileadjustment.
 19. The computer program product of claim 16, whereinselecting the at least one piece of additional content from the corpusof additional content based on the calculated multi-sensory regionfurther comprises: determining a sense associated with the selected atleast one piece of additional content; and determining a distance fromthe originating location to the monitored travel location is less thanthe maximum sense distance value corresponding with the determinedsense.
 20. The computer program product of claim 19, wherein determiningthe sense associated with the selected at least one piece of additionalcontent comprises comparing a textual content of the selected at leastone piece of additional content to a word mapping.